P2-001
The interest in post-lithium batteries as an alternative to lithium-ion batteries boosted recently due to their substantial abundance, low cost, and sustainability. However, safety remains a critical barrier. With seven Accelerating Rate Calorimeters and extremely sensible Tian-Calvet calorimeters, the Battery Calorimeter Laboratory at the IAM-AWP of KIT offers the evaluation of thermodynamic, thermal and safety […]
P2-075
The expansion of use cases of high-power lithium-ion batteries (LIBs) lead to an enormous growth of battery industry. Fast-charging of these batteries is one major issue cell manufacturers and users are both facing nowadays. Faster C-rates enable shorter charging periods but also can lead to cell ageing and in worst cases to safety critical states […]
P2-020
Silicon is considered one of the most promising anode materials for next generation batteries due to its extremely high theoretical capacity of about 3579 mAh g⁻¹. However, its practical application in solid state batteries remains challenging because the large lithiation induced volume expansion exceeding 300% causes severe mechanical stress, cracking, and degradation of the electrode–electrolyte […]
P3-006
Data-driven remaining useful life (RUL) prediction depends on large, high-quality datasets. To address data scarcity, we propose a meta-learning framework for fast-adaptive early-stage RUL prediction of lithium-ion batteries, exploiting only a limited number of degradation profiles to learn a transferable initialization that can quickly adapt to unseen cells and supports robust recursive multi-step forecasting. Test […]
P2-034_Jimoh
The growing demand for high-performance and safe energy storage systems in both stationary and automotive sectors has driven significant attention toward next-generation lithium-ion battery technologies [1]. Among these, semi-solid-state batteries (SSSBs) have emerged as a promising alternative, combining features of conventional lithium-ion batteries (LIBs) and all-solid-state batteries (ASSBs) to offer improved energy density and safety […]
P2-076_Weydahl
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P3-022_Gandiaga
This work presents a methodology for developing State of Charge (SoC) estimation algorithms for lithium-ion (Li-ion) and sodium-ion (Na-ion) batteries using Long Short-Term Memory (LSTM) recurrent neural networks combined with Transfer Learning (TL). Accurate SoC estimation is a key requirement for advanced Battery Management Systems (BMS), as internal battery states such as SoC, State of […]
P4-046_Frenz
The storage of large quantities of Li-ion batteries is necessary in various industries and warehouses around the world. However, an increasing number of fire incidents is continuing to raise questions on the risk and safety of storage configurations of Li-ion batteries, because the fires have the potential to spread rapidly with minor chance for firefighting […]
P1-039
For applications that require both high energy densities and a long cycle life with high safety standards from their batteries, lithium-ion batteries (LIBs) containing liquid electrolytes are still the cell chemistry of choice. The capacity limiting component in common LIBs is the lithium metal oxide cathode. The most prominent cathode material used for high energy […]
P4-009_Berger
State estimators play a crucial role in ensuring safe and efficient battery operation in real-world applications. Insufficient algorithms pose safety risks, user dissatisfaction, and accelerated battery degradation. However, the development and validation of state estimation algorithms for battery management systems (BMS) remains a complex challenge. A review of the literature reveals significant gaps, particularly regarding […]